Jon Myer Podcast

The episode delivers several substantive insights about agentic AI implementation that go beyond surface-level hype, including the distinction between reactive (generative) and proactive (agentic) approaches, the concept of 'pilot purgatory,' and the critical insight that data governance and clean foundational infrastructure are more important than the AI models themselves. However, there is considerable repetition of the same core principles across the 34-minute runtime, and some sections meander without advancing new ideas.

“The question you should be asking is the same as you would, as if you were employing a human being. Does it have the knowledge, the context, does it have access to the tooling to do the job and can we give articulate instructions for it to execute that job effectively?”

“The reality is the AI part is more often than not the most simple part of it. And the integration touch points, the access to data, the policies, the data cleanliness, how we're going to manage that, uh, um, as we start to feed the model the context, give it access to the tooling - that foundational layer can um, be a heavy lift and it generally is a heavy lift.”

The guests make some fresh framings - particularly the vending machine vs. sous chef analogy for generative vs. agentic AI, and the distinction between 'human in the loop' vs. 'human on the loop' - but most core claims are variations on familiar technology implementation wisdom (start with business outcomes, avoid pilot purgatory, ensure data quality). The framing of governance as an enabler rather than a constraint is useful but not groundbreaking.

“imagine generative AI as a vending machine in which you can, you know, purchase uh, a uh, uh, soda or uh, a candy bar... from an agentic AI perspective. You, you are a sous chef, you have to you know, prepare the dish.”

“human on the loop is looking at that process from an observability perspective. He's watching for sentiment, he's watching for outliers, and he's watching it, if you like, uh, holistically above the process and knows when to intervene”

Christopher and Diego from epiuse appear to be experienced AI implementation practitioners with real project delivery background (they reference 350 agentic AI use cases at a bank, pilot purgatory patterns, exception handling in ERP systems). However, the transcript provides minimal biographical detail or evidence of exceptional seniority or track record, and the hosts don't establish their credentials compellingly. They sound competent but not demonstrably top-tier in their field.

“Christopher and Diego from epiuse, who bring deep experience in AI architecture, governance and enterprise implementation”

“from our experience, most of the projects are getting wrong”

The episode includes some specific examples (invoice reconciliation, IT support, HR onboarding, email drafting, purchase order matching) and one concrete data point (40% of AI projects fail by 2027; one bank implemented 350 use cases). However, most claims lack numbers, timelines, or named companies. The invoice matching example is detailed but is one use case; broader claims about enterprise AI adoption remain largely abstract.

“One uh, in which I would like to cover. It's a bank that implement 350 use cases of agentic AI. And it's interesting how the most successful ones were the ones talking and implementing simple processes, IT support, you know, HR onboarding, uh, uh, uh, invoice reconciliation.”

“40% of the AI projects will fail”

The host (John Meyer) asks reasonable opening and framing questions but rarely pushes back, drill down into specifics, or challenge the guests' assertions. Most exchanges are collaborative and explanatory rather than adversarial or probing. The questions tend to invite elaboration on points already made rather than introduce genuine tension or test the guests' thinking. There is little evidence of the host having done independent research or prepared sharp follow-ups.

“Diego, There's a lot of confusion around agentic AI right now. For from what you're seeing, what are most companies getting wrong at the very start?”

“Christopher, you actually touched on it just shortly about the implementation, what it's going to do, why it's going to do it. Do you feel that everybody should take a step back”